Co-expression network analysis of Down's syndrome based on microarray data

Integrated Quantitative Neuro-Transcriptome Analysis of Several Brain Areas in Human Trisomy 21

Background: Although Down syndrome (DS) is the most frequent human chromosomal disorder and it causes mainly intellectual disability, its clinical presentation is complex and variable. Objective: We aimed to analyze and compare the transcriptome disruption in several brain areas from individuals with DS and euploid controls as a new approach to consider a global systemic differential disruption of gene expression beyond chromosome 21. Methods: We used data from a DNA microarray experiment with ID GSE59630 previously deposited in the GEO DataSet of NCBI database. The array contained log2 values of 17,537 human genes expressed in several aeras of the human brain. We calculated the differential gene expression (Z-ratio) of all genes. Results: We found several differences in gene expression along the DS brain transcriptome, not only in the genes located at chromosome 21 but in other chromosomes. Moreover, we registered the lowest Z-ratio correlation between the age ranks of 16–22 weeks of gestation and 39–42 years (R2 = 0.06) and the highest Z-ratio correlation between the age ranks of 30–39 years and 40–42 years (R2 = 0.89). The analysis per brain areas showed that the hippocampus and the cerebellar cortex had the most different gene expression pattern when compared to the brain as a whole. Conclusions: Our results support the hypothesis of a systemic imbalance of brain protein homeostasis, or proteostasis network of cognitive and neuroplasticity process, as new model to explain the important effect on the neurophenotype of trisomy that occur not only in the loci of chromosome 21 but also in genes located in other chromosomes.

Role of long non-coding RNAs in Down syndrome patients: a transcriptome analysis study

Down syndrome (DS) is defined by the presence of a third copy of chromosome 21. Several comorbidities can be found in these patients, such as intellectual disability (ID), muscle weakness, hypotonia, congenital heart disease, and autoimmune diseases. The molecular mechanisms playing a role in the development of such comorbidities are still unclear. The regulation and expression of genes that map to chromosome 21 are dynamic and complex, so it is important to perform global gene expression studies with high statistical power to fully characterize the transcriptome in DS patients. This study was undertaken to evaluate mRNAs and lncRNA expression in patients with DS versus a matched cohort of healthy subjects. RNA sequencing was used to perform this transcriptome study. Differential expression analysis revealed 967 transcripts with padj ≤ 0.05. Among them, 447 transcripts were differentially expressed in patients with DS compared to controls. Particularly, 203 transcripts were down expressed (151 protein-coding mRNAs, 45 lncRNAs, 1 microRNA, 1 mitochondrial tRNA, 1 ribozyme, and 1 small nuclear RNA) and 244 were over expressed (210 protein-coding mRNAs and 34 lncRNAs). Interestingly, deregulated lncRNAs are involved in pathways that play a role in developmental disorders, neurological diseases, DNA replication and repair mechanisms, and cancer development in DS patients. In conclusion, these results suggest a role of lncRNAs in the phenotype of DS patients.

CCR3 gene overexpression in patients with Down syndrome

Chromosome 21 trisomy or Down syndrome (DS) is the most common genetic cause of intellectual disability (ID). DS is also associated with hypotonia, muscle weakness, autoimmune diseases, and congenital heart disease. C-C chemokine receptor type 3 (CCR3) plays a role in inflammatory, autoimmune, and neuronal migration mechanisms. The present study aimed to evaluate the expression of the CCR3 gene by NGS and qRT-PCR in patients with DS and normal controls (NC). The CCR3 gene was over-expressed in DS patients compared to NC. These data suggest that an over-expression of the CCR3 gene is associated with the phenotype of patients with DS.

Bioinformatics Investigation and Contribution of Other Chromosomes Besides Chromosome 21 in the Risk of Down Syndrome Development

INTRODUCTION

Down syndrome as a genetic disorder is a popular research topic in molecular studies. One way to study Down syndrome is via bioinformatics.

METHODS

In this study, a gene expression profile from a microarray study was selected for more investigation.

RESULTS

The study of Down syndrome patients shows specific genes with differential expression and network centrality properties. These genes are introduced as RHOA, FGF2, FYN, and CD44, and their level of expression is high in these patients.

CONCLUSION

This study suggests that besides chromosomes 21, there are additional contributing chromosomes to the risk of Down syndrome development. Besides, these genes could be used for clinical studies after more analysis.

Long non-coding RNA GAS5 expression in patients with Down syndrome

Trisomy 21, also known as Down Syndrome (DS), is the most common chromosome abnormality and causes intellectual disability. Long non-coding RNA (lncRNA) growth arrest-specific 5 (GAS5), whose differential expression has recently been reported in patients with Klinefelter syndrome, has been addressed to play a role in the development of inflammatory and autoimmune diseases, vascular endothelial cells apoptosis and atherosclerosis, all being common features in patients with DS. Therefore, the aim of this study was to assess the lncRNA GAS5 expression profile in DS patients and in controls. lncRNA GAS5 levels were evaluated by qRT-PCR assay in 23 patients with DS and 23 age-matched controls. A significant lncRNA GAS5 down-regulation was observed in patients with DS by RT-PCR analysis, The RNA sequencing experiments confirmed the qRT-PCR data. LncRNA GAS5 down-expression may play a role in the development of some typical features of the patients with DS and, particularly, in inflammatory and autoimmune diseases.

Differentially expressed gene (DEG) based protein-protein interaction (PPI) network identifies a spectrum of gene interactome, transcriptome and correlated miRNA in nondisjunction Down syndrome

Down syndrome, a genetic disorder of known attribution reveals several types of brain abnormalities resulting in mental retardation, inadequacy in speech and memory. In this study, we have presented a consolidative network approach to comprehend the intricacy of the associated genes of Down syndrome. In this analysis, the differentially expressed genes (DEG's) were identified and the central networks were constructed as upregulated and downregulated. Subsequently, GNB5, CDC42, SPTAN1, GNG2, GNAZ, PRKACB, SST, CD44, FGF2, PHLPP1, APP, and FYN were identified as the candidate hub genes by using topological parameters. Later, Fpclass a PPI tool identified WASP gene, a co-expression interacting partner with highest network topology. Moreover, an enhanced enrichment pathway namely Opioid signaling was obtained using ClueGo, depicting the roles of the hub genes in signaling and neuronal mechanisms. The transcriptional regulatory factors and the common miRNA connected to them were identified by using MatInspector and miRTarbase. Later, a regulatory network constructed showed that PLAG, T2FB, CREB, NEUR, and GATA were the most commonly connected transcriptional factors and hsa-miR-122-5p was the most prominent miRNA. In a nutshell, these hub genes and the enriched pathway could help understand at a molecular level and eventually used as therapeutic targets for Down syndrome.

Transcriptome Profiling Uncovers Potential Common Mechanisms in Fetal Trisomies 18 and 21

Human trisomies have recently been investigated using transcriptomics approaches to identify the gene expression (GE) signatures characteristic of each of these specific aneuploidy conditions. We hypothesized that the viability of cells with gross genomic imbalances might be associated with the activation of resilience mechanisms that are common to different trisomies and that are reflected by specific shared GE patterns. We report in this article our microarray GE analyses of amniocytes from fetuses with viable trisomy conditions, trisomy 21 or trisomy 18, to detect such common expression signatures. Comparative analysis of significantly differentially expressed genes in trisomies 18 and 21 revealed six dysregulated genes common to both: OTUD5, ADAMTSL1, TADA2A, PPID, PIAS2, and MAPRE2. These genes are involved in ubiquitination, protein folding, cell proliferation, and apoptosis. Pathway-based enrichment analyses demonstrated that both trisomies showed dysregulation of the PI3K/AKT pathway, cell cycle G2/M DNA damage checkpoint regulation, and cell death and survival, as well as inhibition of the upstream regulator TP53. Our data collectively suggest that trisomies 18 and 21 share common functional GE signatures, implying that common mechanisms of resilience might be activated in aneuploid cells to resist large genomic imbalances. To the best of our knowledge, this is the first study to use global GE profiling data to identify potential common mechanisms in fetal trisomies. Studies of other trisomies using transcriptomics and multiomics approaches might further clarify mechanisms activated in trisomy syndromes.

Integrated Quantitative Transcriptome Maps of Human Trisomy 21 Tissues and Cells

Down syndrome (DS) is due to the presence of an extra full or partial chromosome 21 (Hsa21). The identification of genes contributing to DS pathogenesis could be the key to any rational therapy of the associated intellectual disability. We aim at generating quantitative transcriptome maps in DS integrating all gene expression profile datasets available for any cell type or tissue, to obtain a complete model of the transcriptome in terms of both expression values for each gene and segmental trend of gene expression along each chromosome. We used the TRAM (Transcriptome Mapper) software for this meta-analysis, comparing transcript expression levels and profiles between DS and normal brain, lymphoblastoid cell lines, blood cells, fibroblasts, thymus and induced pluripotent stem cells, respectively. TRAM combined, normalized, and integrated datasets from different sources and across diverse experimental platforms. The main output was a linear expression value that may be used as a reference for each of up to 37,181 mapped transcripts analyzed, related to both known genes and expression sequence tag (EST) clusters. An independent example in vitro validation of fibroblast transcriptome map data was performed through “Real-Time” reverse transcription polymerase chain reaction showing an excellent correlation coefficient (r = 0.93, p < 0.0001) with data obtained in silico. The availability of linear expression values for each gene allowed the testing of the gene dosage hypothesis of the expected 3:2 DS/normal ratio for Hsa21 as well as other human genes in DS, in addition to listing genes differentially expressed with statistical significance. Although a fraction of Hsa21 genes escapes dosage effects, Hsa21 genes are selectively over-expressed in DS samples compared to genes from other chromosomes, reflecting a decisive role in the pathogenesis of the syndrome. Finally, the analysis of chromosomal segments reveals a high prevalence of Hsa21 over-expressed segments over the other genomic regions, suggesting, in particular, a specific region on Hsa21 that appears to be frequently over-expressed (21q22). Our complete datasets are released as a new framework to investigate transcription in DS for individual genes as well as chromosomal segments in different cell types and tissues.